Book Image

Intelligent Workloads at the Edge

By : Indraneel Mitra, Ryan Burke
Book Image

Intelligent Workloads at the Edge

By: Indraneel Mitra, Ryan Burke

Overview of this book

The Internet of Things (IoT) has transformed how people think about and interact with the world. The ubiquitous deployment of sensors around us makes it possible to study the world at any level of accuracy and enable data-driven decision-making anywhere. Data analytics and machine learning (ML) powered by elastic cloud computing have accelerated our ability to understand and analyze the huge amount of data generated by IoT. Now, edge computing has brought information technologies closer to the data source to lower latency and reduce costs. This book will teach you how to combine the technologies of edge computing, data analytics, and ML to deliver next-generation cyber-physical outcomes. You’ll begin by discovering how to create software applications that run on edge devices with AWS IoT Greengrass. As you advance, you’ll learn how to process and stream IoT data from the edge to the cloud and use it to train ML models using Amazon SageMaker. The book also shows you how to train these models and run them at the edge for optimized performance, cost savings, and data compliance. By the end of this IoT book, you’ll be able to scope your own IoT workloads, bring the power of ML to the edge, and operate those workloads in a production setting.
Table of Contents (17 chapters)
1
Section 1: Introduction and Prerequisites
3
Section 2: Building Blocks
10
Section 3: Scaling It Up
13
Section 4: Bring It All Together

Knowledge check

Before moving on to the next chapter, test your knowledge by answering these questions.

The answers can be found at the end of the book:

  1. What are three network topologies that are common in edge solutions? Which one is implemented by the HBS hub device and appliance monitoring kit?
  2. True or false: IoT Greengrass operates at the physical layer (that is, layer 1) of the OSI model.
  3. What is the benefit of using a publish/subscribe model to exchange messages?
  4. True or false: IoT Greengrass can act as both a messaging client and a messaging broker.
  5. Is a message such as {"temperature": 70} an example of structured data or unstructured data? Is it serializable?
  6. Is image data captured from a camera an example of structured data or unstructured data? Is it serializable?
  7. What do you think is the worst-case scenario if your home network router was compromised by an attacker but was still processing traffic as normal?
  8. What is a mitigation...